PURPOSES: In this study, algorithms were proposed for determining the crack condition of an asphalt pavement image using deep learning methods. METHODS: For the configuration of a deep learning network, the study used a Convolution Neural Network and You Only Look Once algorithms. To obtain input data for analysis, a camera was mounted on the bonnet of the vehicle to obtain images of asphalt pavement and to mark the ground-truth cracks in the asphalt pavement image. In addition, an algorithm suitable for the automatic determination function of Deep Learning was proposed in order to calculate the crack ratio and crack rating. RESULTS: The result of analysis showed that the recall rate of cracks in this system was higher from FPPW 5.0E-06 to 96.03%. Furthermore, the accuracy of the grading system was found to be 100%, enabling the determination of very accurate ratings. The rate of processing per image was 0.4448 seconds on average, and the real-time analysis of pavement images presented no problem because the assessment took place within a short time. CONCLUSIONS : Applying this system to the pavement management system is expected to reduce the time required in finishing work and to determine a quantitative crack rating.

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS: For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS: The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination (R2) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as R2 had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

PURPOSES: This study examines the performance changes of road networks according to the strength of a disaster, and proposes a method for estimating the quantitative resilience according to the road-network performance changes and damage scale. This study also selected highinfluence road sections, according to disasters targeting the road network, and aimed to analyze their hazard resilience from the network aspect through a scenario analysis of the damage recovery after a disaster occurred. METHODS: The analysis was conducted targeting Sejong City in South Korea. The disaster situation was set up using the TransCAD and VISSIM traffic-simulation software. First, the study analyzed how road-network damage changed the user’s travel pattern and travel time, and how it affected the complete network. Secondly, the functional aspects of the road networks were analyzed using quantitative resilience. Finally, based on the road-network performance change and resilience, priority-management road sections were selected. RESULTS: According to the analysis results, when a road section has relatively low connectivity and low traffic, its effect on the complete network is insignificant. Moreover, certain road sections with relatively high importance can suffer a performance loss from major damage, for e.g., sections where bridges, tunnels, or underground roads are located, roads where no bypasses exist or they exist far from the concerned road, including entrances and exits to suburban areas. Relatively important roads have the potential to significantly degrade the network performance when a disaster occurs. Because of the high risk of delays or isolation, they may lead to secondary damage. Thus, it is necessary to manage the roads to maintain their performance. CONCLUSIONS : As a baseline study to establish measures for traffic prevention, this study considered the performance of a road network, selected high-influence road sections within the road network, and analyzed the quantitative resilience of the road network according to scenarios. The road users’passage-pattern changes were analyzed through simulation analysis using the User Equilibrium model. Based on the analysis results, the resilience in each scenario was examined and compared. Sections where a road’s performance loss had a significant influence on the network were targeted. The study results were judged to become basic research data for establishing response plans to restore the original functions and performance of the destroyed and damage road networks, and for selecting maintenance priorities.

PURPOSES : This study is aimed at development of a stochastic pavement deterioration forecasting model using National Highway Pavement Condition Index (NHPCI) to support infrastructure asset management. Using this model, the deterioration process regarding life expectancy, deterioration speed change, and reliability were estimated. METHODS: Eight years of Long-Term Pavement Performance (LTPP) data fused with traffic loads (Equivalent Single Axle Loads; ESAL) and structural capacity (Structural Number of Pavement; SNP) were used for the deterioration modeling. As an ideal stochastic model for asset management, Bayesian Markov multi-state exponential hazard model was introduced. RESULTS: The interval of NHPCI was empirically distributed from 8 to 2, and the estimation functions of individual condition indices (crack, rutting, and IRI) in conjunction with the NHPCI index were suggested. The derived deterioration curve shows that life expectancies for the preventive maintenance level was 8.34 years. The general life expectancy was 12.77 years and located in the statistical interval of 11.10-15.58 years at a 95.5% reliability level. CONCLUSIONS : This study originates and contributes to suggesting a simple way to develop a pavement deterioration model using the total condition index that considers road user satisfaction. A definition for level of service system and the corresponding life expectancies are useful for building long-term maintenance plan, especially in Life Cycle Cost Analysis (LCCA) work.

PURPOSES: This study proposes the road asset valuation approach using alternative depreciation methods. It has become necessary to have asset management system according to the adoption of accrual basis accounting for governmental financial reporting and the amendment of the road act. Therefore, it is very important to analyze the effect of depreciation methods on road asset value as a basic research for road asset management system. METHODS: The Ministry of Strategy and Finance (MOSF) has mainly performed road asset valuation based on Write down Replacement Cost and Straight Line depreciation method. This study suggests some appropriate asset valuation methods for road assets through case analysis using three depreciation methods: Consumption-based depreciation method, Condition-based depreciation method, and Straight Line depreciation method. A road asset valuation data of national highway route 1 (year 2014) is used to analyze the effect of three depreciation methods on the road asset value. Road assets include land and structures (pavement, bridge, and tunnel). This study mainly focuses on structures such as bridges and tunnels, because according to governmental accounting standards, land and road pavement assets do not depreciate. RESULTS : The main results of this study are as follows. Firstly, overall asset value of national highway route 1 was estimated at 6.97 trillion KRW when MOSF's method (straight-line depreciation method) is applied. Secondly, asset value was estimated at 4.85 trillion KRW on application of consumption-based depreciation method. Thirdly, asset value was estimated at 4.37 trillion KRW when condition-based depreciation method is applied. Therefore, either consumption-based or condition-based depreciation methods would be more appropriate than straight-line depreciation method if we can use the condition data of road assets including land that are available in real time. CONCLUSIONS : Since road assets such as pavements, bridges, and tunnels have various patterns of deterioration and condition monitoring period, it is necessary to consider a specific valuation method according to the condition of each road asset. Firstly, even though road pavements do not depreciate, asset valuation through condition-based depreciation method would be more appropriate when requirements for application of non-depreciation approach are not satisfied. Since bridge and tunnel facilities show various patterns of deterioration and condition monitoring period by type and condition level, consumption-based depreciation method based on deterioration model would be appropriate. Therefore, it is necessary to have a reasonable asset management system to apply condition-based depreciation method and a periodic condition investigation to manage road assets well.

PURPOSES : Long-life asphalt pavements are used widely in developed countries. In order to be able to devise an effective maintenance strategy for such pavements, in this study, we evaluated the performance of the long-life asphalt pavements constructed along the national highways in South Korea. Further, an economic evaluation of the long-life asphalt pavements was performed based on a life-cycle cost analysis. We aimed to devise a model for evaluating the performance of long-life asphalt pavements using the national highway pavement management system (PMS) database as well as for analyzing the economic feasibility of such pavements, in order to promote their use in South Korea. METHODS : The maintenance history and pavement performance data were obtained from the national highway PMS database. The pavement performances for a total of 292 sections of 10 lanes (5 northbound lanes and 5 eastbound lanes) of national highways were used in this study. Models to predict the performances of hot mix asphalt (HMA) and long-life asphalt pavements under two distinct traffic conditions were developed using a simple regression method. Further, the economic feasibility of long-life asphalt pavements was evaluated using the Korea Pavement Management System (KoPMS). RESULTS : We developed service-life prediction models based on the traffic volume and the equivalent of single-axle load and found that long-life asphalt pavements have service lives 50% longer than those of HMA pavements. Further, the results of the economic analysis showed that long-life asphalt pavements are superior in terms of various economic indexes, including user cost, delay cost, total cost, and user benefits, even though their maintenance cost is higher than that of HMA pavements. A comparison of the economic feasibilities of the various groups showed that group A is superior to HMA pavements in all aspects except in terms of the maintenance criterion (crack 20% or higher) as per the NPV index. However, the long-life asphalt pavements in group B were superior in terms of the maintenance criterion (crack 25% or higher) regardless of the economic feasibility. CONCLUSIONS: The service life of long-life asphalt pavements was found to be approximately 50% longer than that of HMA pavements, regardless of the traffic volume characteristics. The economic feasibility of long-life asphalt pavements was evaluated based on the KoPMS. The results of the economic analysis were the following: long-life asphalt pavements are exceptional in terms of almost all factors, such as user cost, delay cost, total cost, and user benefit; however, the exception is the maintenance cost. Further, the economic feasibility of the long-life asphalt pavements in group B was found to be better than that of the HMA pavements (crack 25% or higher).

PURPOSES : This study aims to examine the differences between the existing traffic demand forecasting method and the traffic demand forecasting method considering future regional development plans and new road construction and expansion plans using a four-step traffic demand forecast for a more objective and sophisticated national highway maintenance. This study ultimately aims to present future pavement deterioration and budget forecasting planning based on the examination. METHODS: This study used the latest data offered by the Korea Transport Data Base (KTDB) as the basic data for demand forecast. The analysis scope was set using the Daejeon Metropolitan City’s O/D and network data. This study used a traffic demand program called TransCad, and performed a traffic assignment by vehicle type through the application of a user equilibrium-based multi-class assignment technique. This study forecasted future traffic demand by verifying whether or not a realistic traffic pattern was expressed similarly by undertaking a calibration process. This study performed a life cycle cost analysis based on traffic using the forecasted future demand or existing past pattern, or by assuming the constant traffic demand. The maintenance criteria were decided according to equivalent single axle loads (ESAL). The maintenance period in the concerned section was calculated in this study. This study also computed the maintenance costs using a construction method by applying the maintenance criteria considering the ESAL. The road user costs were calculated by using the user cost calculation logic applied to the Korean Pavement Management System, which is the existing study outcome. RESULTS : This study ascertained that the increase and decrease of traffic occurred in the concerned section according to the future development plans. Furthermore, there were differences from demand forecasting that did not consider the development plans. Realistic and accurate demand forecasting supported an optimized decision making that efficiently assigns maintenance costs, and can be used as very important basic information for maintenance decision making. CONCLUSIONS : Therefore, decision making for a more efficient and sophisticated road management than the method assuming future traffic can be expected to be the same as the existing pattern or steady traffic demand. The reflection of a reliable forecasting of the future traffic demand to life cycle cost analysis (LCCA) can be a very vital factor because many studies are generally performed without considering the future traffic demand or with an analysis through setting a scenario upon LCCA within a pavement management system.